000			05232nam a22002057a 4500
999			_c1310 _d1310
005			20210927145755.0
008			210927b ||||| |||| 00| 0 eng d
020			_a9781118936337
082			_a620.0015196 _bRHI
100			_aRhinehart, R. Russell _93769
245			_aEngineering optimization: applications, methods, and analysis
260			_bWiley India Pvt. Ltd. _aNew Jersey _c2018
300			_axxxvii, 731 p.
365			_aUSD _b130.00
504			_aTABLE OF CONTENTS Section 1 Introductory Concepts 1 Optimization: Introduction and Concepts 2 Optimization Application Diversity and Complexity 3 Validation: Knowing That the Answer Is Right Section 2 Univariate Search Techniques 4 Univariate (Single DV) Search Techniques 5 Path Analysis 6 Stopping and Convergence Criteria: 1-D Applications Section 3 Multivariate Search Techniques 7 Multidimension Application Introduction and the Gradient 8 Elementary Gradient-Based Optimizers: CSLS and ISD 9 Second-Order Model-Based Optimizers: SQ and NR 10 Gradient-Based Optimizer Solutions: LM, RLM, CG, BFGS, RG, and GRG 11 Direct Search Techniques 12 Linear Programming 13 Dynamic Programming 14 Genetic Algorithms and Evolutionary Computation 15 Intuitive Optimization 16 Surface Analysis II 17 Convergence Criteria 2: N-D Applications 18 Enhancements to Optimizers Section 4 Developing Your Application Statements 19 Scaled Variables and Dimensional Consistency 20 Economic Optimization 21 Multiple OF and Constraint Applications 22 Constraints 23 Multiple Optima 24 Stochastic Objective Functions 25 Effects of Uncertainty 26 Optimization of Probable Outcomes and Distribution Characteristics 27 Discrete and Integer Variables 28 Class Variables 29 Regression Section 5 Perspective on Many Topics 30 Perspective 31 Response Surface Aberrations 32 Identifying the Models, OF, DV, Convergence Criteria, and Constraints 33 Evaluating Optimizers 34 Troubleshooting Optimizers Section 6 Analysis of Leapfrogging Optimization 35 Analysis of Leapfrogging Section 7 Case Studies 36 Case Study 1: Economic Optimization of a Pipe System 37 Case Study 2: Queuing Study 38 Case Study 3: Retirement Study 39 Case Study 4: A Goddard Rocket Study 40 Case Study 5: Reservoir 41 Case Study 6: Area Coverage 42 Case Study 7: Approximating Series Solution to an ODE 43 Case Study 8: Horizontal Tank Vapor–Liquid Separator 44 Case Study 9: In Vitro Fertilization 45 Case Study 10: Data Reconciliation Section 8 Appendices Appendix A Mathematical Concepts and Procedures Appendix B Root Finding Appendix C Gaussian Elimination Appendix D Steady-State Identification in Noisy Signals Appendix E Optimization Challenge Problems (2-D and Single OF) Appendix F Brief on VBA Programming: Excel in Office 2013
520			_aAn Application-Oriented Introduction to Essential Optimization Concepts and Best Practices Optimization is an inherent human tendency that gained new life after the advent of calculus; now, as the world grows increasingly reliant on complex systems, optimization has become both more important and more challenging than ever before. Engineering Optimization provides a practically-focused introduction to modern engineering optimization best practices, covering fundamental analytical and numerical techniques throughout each stage of the optimization process. Although essential algorithms are explained in detail, the focus lies more in the human function: how to create an appropriate objective function, choose decision variables, identify and incorporate constraints, define convergence, and other critical issues that define the success or failure of an optimization project. Examples, exercises, and homework throughout reinforce the author’s “do, not study” approach to learning, underscoring the application-oriented discussion that provides a deep, generic understanding of the optimization process that can be applied to any field. Providing excellent reference for students or professionals, Engineering Optimization: Describes and develops a variety of algorithms, including gradient based (such as Newton’s, and Levenberg-Marquardt), direct search (such as Hooke-Jeeves, Leapfrogging, and Particle Swarm), along with surrogate functions for surface characterization Provides guidance on optimizer choice by application, and explains how to determine appropriate optimizer parameter values Details current best practices for critical stages of specifying an optimization procedure, including decision variables, defining constraints, and relationship modeling Provides access to software and Visual Basic macros for Excel on the companion website, along with solutions to examples presented in the book Clear explanations, explicit equation derivations, and practical examples make this book ideal for use as part of a class or self-study, assuming a basic understanding of statistics, calculus, computer programming, and engineering models. Anyone seeking best practices for “making the best choices” will find value in this introductory resource.
650			_aMathematical optimization _9647
650			_aEngineering--Mathematical models _93770
942			_2ddc _cBK